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Optimization of micro machining processes
- 1. Optimization of Micro- machining Processes DR ANAND J KULKARNI SYMBIOSIS CENTER FOR RESEARCH AND INNOVATION, SYMBIOSIS INTERNATIONAL (DEEMED UNIVERSITY), PUNE, INDIA EMAIL: ANAND.KULKARNI@SITPUNE.EDU.IN; KULK0003@NTU.EDU.SG APOORVA SHASTRI, ANIKET NARGUNDKAR SYMBIOSIS INSTITUTE OF TECHNOLOGY, SYMBIOSIS INTERNATIONAL (DEEMED UNIVERSITY), PUNE, INDIA
- 2. Outline ο΅ Non Traditional Machining Processes (NTM) ο΅ Need of Optimization and Techniques ο΅ Electric Discharge Machining (EDM) ο΅ Abrasive Water Jet Machining (AWJM) ο΅ Micro-Milling ο΅ Micro-Drilling with CFRP Application ο΅ Micro-Turning 2
- 3. Manufacturing Processes ο΅ Definition: the application of mechanical, physical, and chemical processes to convert the geometry, properties, and/or shape of raw material into finished parts or products. ο΅ Primary Processes ο΅ Machining Processes ο΅ Metal Forming Processes ο΅ Joining Processes ο΅ Surface Finishing Processes 3
- 4. 20th & 21st Century Applications & Materials ο΅ Aerospace ο΅ Military ο΅ Automobile ο΅ Electronic Gadgets ο΅ Complex Designs, many more, β¦ ο΅ Materials and alloys necessitate ο΅ high strength-to-weight ratio ο΅ high stiffness and toughness ο΅ high heat capacity ο΅ thermal conductivity, etc. 4
- 5. 20th & 21st Century Applications & Traditional Machining Processes ο΅ Traditional Machining Processes, Chip Removal Processes ο΅ Generation of High Temperatures and Stresses ο΅ Challenges: ο΅ Rapid Deterioration of the Cutting Tools ο΅ Inferior Quality of Machined Parts ο΅ Innovative and Complex Designs ο΅ Demanding Tolerance Requirements ο΅ Cost Reduction 5
- 6. Non Traditional Machining (NTM) Processes NTM Processes Mechanical Processes AWJM USM Electro Chemical Processes ECM Electro Thermal Processes EDM Tool based Micro Machining Micro Turning Micro Milling Micro Drilling Sustainable Machining (MQL, Cryogenic) 6
- 7. Need for Optimization ο΅ Dynamic and Market competition driven manufacturing ο΅ Reduced time-to-market: shorter manufacturing time ο΅ Minimal manufacturing costs: efficient use of all the resources ο΅ High and Expected quality of highly customized products ο΅ Growing needs for safety ο΅ Determine optimal process parameter settings ο΅ Productivity ο΅ Quality ο΅ Cost of Production 7
- 9. Cohort: A Self Organizing System 9
- 10. 10
- 11. Cohort Intelligence: Applications ο΅ Heat Exchanger Design ο΅ Healthcare ο΅ Image Processing ο΅ Finite Element Analysis ο΅ Robotics Path Planning ο΅ Control Systems ο΅ Machine Learning ο΅ Logistics & Transportation ο΅ Forming Processes ο΅ Mechanical Engineering Design ο΅ Micro Machining Processes 11
- 12. 12
- 13. Electric Discharge Machining (EDM) ο΅ Controlled Spark-Erosion ο΅ high strength temperature resistant materials and alloys (hardened steel, carbide, etc.) with intricate geometries G Tool (-ve) Work Piece (+ve) Dielectric Medium 13
- 14. EDM: Process Parameters/Variables & Objectives ο΅ The Process Parameters/Variables ο΅ Discharge Current (π1) ο΅ Gap Voltage (π2) ο΅ Pulse on-time (π3) ο΅ Pulse off-time (π4) ο΅ Gap between the work piece and the tool ο΅ Dielectric medium. ο΅ Objectives ο΅ Minimize Surface Roughness (π π) ο΅ Minimize Relative Electrode Wear Rate (π πΈππ ) ο΅ Maximizing Material Removal Rate (ππ π ) G Tool (-ve) Work Piece (+ve) Dielectric Medium 14 Muthuramalingam and Mohan (2015) Gopalakannan and Senthilvelan (2014)
- 15. EDM: Problem Formulations πππ₯ππππ§π ππ π = β235.15 + 39.7π1 + 4.227π2 + 1.569π3 β 1.375π4 β 0.0059π3 2 β 0.536π1 π2 πππππππ§π π π = 31.547 β 0.0618π1 β 0.438π2 + 0.059π3 β 0.59π4 + 0.019π1 π4 β 0.0075π2 π4 πππππππ§π π πΈππ = 196.564 β 24.19π1 β 3.135π2 β 1.781π3 + 0.153π4 + 0.093π1 2 + 0.00149π3 2 + 0.005265π4 2 + 0.464π1 π2 + 0.158π1 π3 + 0.025π1 π4 + 0.029π2 π3 β 0.017π2 π4 β 0.003385π1 π2 π3 7.5 β€ π1β€ 12.5 45 β€ π2β€ 55 50 β€ π3β€ 150 40 β€ π4β€ 60 15 Tzeng and Chen (2013) Shukla and Singh (2017)
- 16. EDM: Solutions to ππ π , π π & π πΈππ Function GA SA PSO Roulette wheel fbest fbetter alienation Multi-CI ππ π Mean 183.37 182.03 183.37 183.26 38.98 38.24 96.45 183.37 S.D. 0.00 2.21 0.00 0.11 0.69 0.71 23.21 0.00 Best 183.37 183.09 183.37 183.35 39.63 39.52 144.32 183.37 Run Time 1.41 2.61 1.81 0.35 0.59 0.64 0.51 1.81 π π Mean 3.55 3.67 3.55 3.61 3.60 3.61 5.99 3.55 S.D. 0.00 0.16 0.05 0.03 0.02 0.03 1.76 0.00 Best 3.55 3.58 3.55 3.55 3.55 3.55 4.06 3.55 Run Time 1.45 2.69 1.89 0.38 0.6 0.63 0.53 1.89 π πΈππ % Mean 1.30 Γ 10-8 6.82 Γ 10-4 3.73 Γ 10-9 8.53 Γ 10-5 2.96 Γ 10-5 7.90 Γ 10-6 2.37 Γ 10-7 1.85 Γ 10-8 S.D. 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 Best 1.22 Γ 10-7 1.36 Γ 10-2 8.95 Γ 10-9 2.43 Γ 10-4 1.49 Γ 10-4 4.15 Γ 10-5 9.79 Γ 10-7 9.50 Γ 10-9 Run Time 1.7 2.8 1.42 0.42 0.62 0.66 0.57 1.93 16
- 17. EDM: Solutions to ππ π , π π & π πΈππ πππ₯ππππ§π ππ π πππππππ§π π π πππππππ§π π πΈππ 17
- 18. EDM: Solutions to ππ π , π π & π πΈππ Problem RSM Tzeng & Chen (2013) BPNN Tzeng & Chen (2013) FA Shukla & Singh (2017)b roulette wheel fbest fbetter alienation GA SA PSO Multi-CI ππ π 157.39 159.70 181.67 183.35 39.63 39.52 144.32 183.35 183.09 183.37 183.37 π π 7.38 7.04 3.67 3.55 3.55 3.55 4.06 3.55 3.58 3.55 3.55 REWR % 7.63 6.21 6.32 Γ10-5 2.43 Γ10-4 2.96 Γ10-5 7.90 Γ10-6 9.79 Γ10-9 1.22 Γ10-7 2.43 Γ10-4 1.85 Γ10-9 9.50 Γ10-9 18
- 19. Abrasive Water Jet Machining (AWJM) 19
- 20. Abrasive Water Jet Machining (AWJM) ο΅ Very high velocity fine abrasive particles impinge on the work piece ο΅ Effective for hard, brittle material, tough, hard to machine materials (titanium, stainless steel, high-strength temperature-resistant alloys, ceramics, refractories, fiber-reinforced composites, super alloys, etc.) Gupta et al 2017 20
- 21. AWJM: Process Parameters/ Variables & Objectives ο΅ The Process Parameters/Variables ο΅ Work Piece Thickness (π1) ο΅ Nozzle Diameter (π2) ο΅ Standoff Distance (π3) ο΅ Traverse Speed (π4) ο΅ Objectives ο΅ Minimize Taper Angle ππππ (Geometry) ο΅ Minimize Surface Roughness π π (Surface Finish) 21
- 22. AWJM: Problem Formulations πππππππ§π π π = β23.309555 + 16.6968π1 + 26.9296π2 + 0.0587π3 + 0.0146π4 β 5.1863π2 2 β 10.4571π1 π2 β 0.0534π1 π3 β 0.0103π1 π4 + 0.0113π2 π3 β 0.0039π2 π4 πππππππ§π ππππ = β1.15146 + 0.70118π1 + 2.72749π2 + 0.00689π3 β 0.00025π4 + 0.00386π2 π3 β 0.93947π2 2 β 0.25711a1a2 β 0.00314a1a3 β 0.00249a1a4 + 0.00196a2a4 β 0.00002a3a4 β 0.00001a3 2 0.9 β€ π1β€ 1.25 0.95 β€ π2β€ 1.5 20 β€ π3β€ 96 200 β€ π4β€ 600 Shukla and Singh (2017) 22
- 23. AWJM: Solutions to ππππ & π π Problem Expt (Kechagias, 2012) Regression (Kechagias, 2012) FA Shukla and Singh (2017) roulette wheel fbest fbetter alienation GA SA PSO Multi-CI Gulia and Nargundkar (2019) π π 5.80 5.41 4.44 4.38 4.38 4.38 4.38 4.38 4.61 4.39 4.38 ππππ 0.85 090 0.37 0.34 0.34 0.34 0.34 0.33 0.35 0.43 0.33 Function GA SA PSO Multi-CI π π Mean 4.43 4.86 4.39 4.38 S.D. 0.03 0.12 0.22 0.00 Best 4.38 4.61 4.75 4.38 Run Time 1.62 2.63 1.78 4.63 ππππ Mean 0.33 0.41 0.43 0.33 S.D. 0.01 0.04 0.00 0.01 Best 0.33 0.36 0.43 0.33 Run Time 1.48 2.8 1.52 3.89 23
- 24. AWJM: Solutions to ππππ & π π Minimize Taper Angle ππππ (Geometry) Minimize Surface Roughness π π (Surface Finish) 24
- 25. Tool Based Micro-machining Processes ο΅ Todayβs manufacturing field involve ο΅ increased number of functions ο΅ Miniaturization of parts/dimensions ο΅ Advantages of tool based micromachining ο΅ Productivity ο΅ Efficiency ο΅ Flexibility ο΅ Cost effectiveness 25
- 26. Tool Based Micro-machining Processes Methods of Micro Fabrication Material Deposition (Additive) Physical Vapour Deposition Chemical Vapour Deposition Lithography Material Removal/Mecha nical Processes (Subtractive) Conventional Tool Based Processes such as Micro Turning, Micro Drilling and Micro Milling Advanced Processes such as Micro-EDM, Micro- ECM 26
- 27. Micro-Milling 27
- 28. Micro-Milling: Tool Diameter β€ 1ππ ο΅ Miniature featured objects: Meso (500 Β΅m-10mm) and Micro Scale (1-500 Β΅m) ο΅ Aeronautical ο΅ Biomedical ο΅ Automobile ο΅ Optical ο΅ Nuclear ο΅ Semiconductor sector ο΅ Materials: Ceramics, Specialized Metals, Polymers, etc. 28
- 29. Micro-Milling: Process Parameters/ Variables & Objectives ο΅ The Process Parameters/Variables ο΅ Cutting Speed (π1) ο΅ Feed (π2) ο΅ Objectives ο΅ Surface Roughness (π π) ο΅ Machining Time (ππ‘) 29 Kumar et al. (2014)
- 30. Micro-Milling: Process Parameters/ Variables & Objectives ο΅ For tool diameter 0.7 mm πππππππ§π π π = β0.455378 + 0.00027π1 + 0.016422π2 β 0.000077π1 π2 πππππππ§π ππ‘ = 17.7164 β 0.0002π1 β 4.8404π2 + 0.0001π1 π2 ο΅ For tool diameter 1 mm πππππππ§π π π = β0.208871 + 0.000144π1 + 0.019571π2 πππππππ§π ππ‘ = 20.2906 β 0.0015π1 β 5.8369π2 + 0.0006π1 π2 1500 β€ π1 β€ 2500 1 β€ π2 β€ 3 Kumar et al. (2014) 30
- 31. Micro-Milling: Solutions to π π & ππ‘ Micro Machining Processes Cutter Diameter Objective Function Algorithms Applied GA SA PSO Variations of CI Multi-CIroulette wheel fbest fbetter alienation Micro Milling 0.7 mm π π Mean 0.00 0.13 0.00 0.00 0.12 0.19 0.00 0.00 S.D. 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 Best 0.00 0.13 0.00 0.00 0.09 0.19 0.00 0.00 Run Time 1.40 2.60 1.12 0.06 0.05 0.06 0.14 0.21 ππ‘ Mean 3.35 3.42 3.34 3.35 3.35 3.35 3.35 3.35 S.D. 0.01 0.00 0.00 0.00 0.01 0.01 0.00 0.00 Best 3.35 3.42 3.35 3.35 3.35 3.35 3.35 3.35 Run Time 1.44 2.62 0.74 0.04 0.04 0.04 0.10 0.16 1 mm π π Mean 0.03 0.16 0.03 0.03 0.11 0.21 0.03 0.03 S.D. 0.00 0.00 0.00 0.01 0.02 0.00 0.01 0.00 Best 0.03 0.15 0.03 0.03 0.06 0.21 0.03 0.03 Run Time 1.78 2.76 0.98 0.06 0.05 0.06 0.11 0.39 ππ‘ Mean 3.23 3.47 3.23 3.23 3.23 3.23 3.23 3.23 S.D. 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 Best 3.23 3.44 3.23 3.23 3.23 3.23 3.23 3.23 Run Time 1.72 2.77 1.07 0.05 0.06 0.05 0.10 0.37 31
- 32. Micro-Milling: Solutions to π π & ππ‘ Tool diameter 0.7 mm π π & ππ‘ Tool diameter 1 mm π π & ππ‘ 32
- 34. Micro-Drilling ο΅ PCB circuits ο΅ Microprocessor ο΅ automotive industry ο΅ Fuel Injectors ο΅ Fasteners for Micro-jacks and Micro-pins ο΅ Hole quality ο΅ Reduced Burr Thickness and height ο΅ Surface Finish ο΅ Durability ο΅ Precision ο΅ Assembly problems Challenges in De-burring: β’ Poor Accessibility of Burr Area β’ Tight Tolerance Crown burr - very low feed rate and high speed Transient burr - higher feed rate and higher speed Uniform burr - not solely dependent on feed rate and speed but tool diameter and tool type Pansari et al. 2019 34
- 35. Micro-Drilling: Process Parameters/ Variables & Objectives ο΅ Important Parameters: ο΅ Tool Diameter ο΅ Spindle Speed (π1) ο΅ Tool Helix Angle ο΅ Feed Rate (π2) ο΅ Hole quality ο΅ Burr Thickness (π΅π‘) ο΅ Burr Height (π΅β) Choudhary, H. (2007) 35
- 36. Micro-Drilling: Process Parameters/ Variables & Objectives ο΅ For tool diameter 0.5mm πππππππ§π π΅β = 420.94 β 0.234π1 β 99.91π2 + 6.5510β5 π1 2 + 22.152π2 2 πππππππ§π π΅π‘ = 90.57 β 0.049π1 β 27.12π2 + 1.3210β5 π1 2 + 5.54π2 2 ο΅ For tool diameter 0.6 mm πππππππ§π π΅β = 369.67 β 0.028π1 β 156.79π2 + 6.64 Γ 10β6 π1 2 + 23.162π2 2 πππππππ§π π΅π‘ = 35.34 β 0.019π1 β 0.59π2 + 6.44 Γ 10β6 π1 2 + 0.51π2 2 ο΅ For tool diameter 0.8 mm πππππππ§π π΅β = 106.116 β 0.13π1 β 6.62π2 + 1.49 Γ 10β6 π1 2 + 4.75π2 2 πππππππ§π π΅π‘ = 59.79 β 0.024π1 β 11.3π2 β 7.78 Γ 10β6 π1 2 + 2.18π2 2 ο΅ For tool diameter 0.9 mm πππππππ§π π΅β = 450.7 β 0.09π1 β 38.48π2 + 2.34 Γ 10β5 π1 2 + 5.03π2 2 πππππππ§π π΅π‘ = 80.07 β 0.040π1 β 14.81 π2 + 1.516 Γ 10β5 π1 2 + 4.65π2 2 1000 β€ π1 β€ 2500 1 β€ π2 β€ 4 Pansari et al. 2019 36
- 37. Micro-Drilling: Solutions to π΅π‘ & π΅β Micro Machining Processes Cutter Diameter Objective Function Algorithms Applied GA SA PSO Variations of CI Multi-CI roulette wheel Fbest fbetter alienation Micro Drilling 0.5 mm π΅β Mean 99.29 134.13 99.29 99.29 99.29 99.29 99.29 99.29 S.D. 0.00 1.30 0.00 0.00 0.00 0.00 0.00 0.00 Best 99.29 131.79 99.29 99.29 99.29 99.29 99.29 99.29 Run Time 1.47 2.60 1.14 0.04 0.04 0.04 0.08 0.14 π΅π‘ Mean 11.91 21.13 11.90 11.91 11.91 11.91 11.91 11.91 S.D. 0.00 1.22 0.00 0.00 0.00 0.00 0.00 0.00 Best 11.91 18.14 11.90 11.91 11.91 11.91 11.91 11.91 Run Time 1.43 2.62 0.87 0.04 0.04 0.04 0.08 0.15 0.6mm π΅β Mean 74.83 75.30 74.81 74.81 74.81 74.81 74.81 74.81 S.D. 0.04 0.24 0.00 0.00 0.00 0.00 0.00 0.00 Best 74.81 74.91 74.81 74.81 74.81 74.81 74.81 74.81 Run Time 1.62 2.65 0.76 0.04 0.04 0.04 0.08 0.14 π΅π‘ Mean 21.25 22.65 21.25 21.25 21.25 21.25 21.25 21.25 S.D. 0.01 1.91 0.00 0.00 0.00 0.00 0.00 0.00 Best 21.25 21.25 21.25 21.25 21.25 21.25 21.25 21.25 Run Time 1.64 2.66 1.23 0.04 0.04 0.05 0.01 0.14 0.8mm π΅β Mean 235.78 235.73 235.74 235.74 235.74 235.74 235.74 235.74 S.D. 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Best 235.74 235.73 235.74 235.74 235.74 235.74 235.74 235.74 Run Time 1.73 2.67 1.34 0.06 0.05 0.06 0.12 0.17 π΅π‘ Mean 26.64 32.90 26.64 26.64 26.64 26.64 26.64 26.64 S.D. 0.00 0.6 0.00 0.00 0.00 0.00 0.00 0.00 Best 26.64 31.70 26.64 26.64 26.64 26.64 26.64 26.64 Run time 1.68 2.71 0.88 0.035 0.036 0.04 0.076 0.16 0.9mm π΅β Mean 305.07 307.74 305.07 305.07 305.07 305.07 305.07 305.07 S.D. 0.00 2.46 0.00 0.00 0.00 0.00 0.00 0.00 Best 305.07 305.39 305.07 305.07 305.07 305.07 305.07 305.07 Run Time 1.42 2.60 1.12 0.04 0.03 0.03 0.07 0.08 π΅π‘ Mean 41.89 43.09 41.89 41.89 41.89 41.89 41.89 41.89 S.D. 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 Best 41.89 43.01 41.89 41.89 41.89 41.89 41.89 41.89 Run Time 1.47 2.64 0.96 0.04 0.03 0.04 0.07 0.10 37
- 38. Micro-Drilling of CFRP Composites for Aerospace App 38
- 39. Micro-Drilling of CFRP Composites for Aerospace App ο΅ Minimization of Cutting Forces in x, y and z directions induced in micro drilling of Carbon Fiber Reinforced Plastic (CFPR) composite materials for aerospace applications. ο΅ CFRP Properties ο΅ High Stiffness ο΅ High Strength to Weight Ratio ο΅ Good Damping Capacity 39
- 40. Process Parameters/ Variables & Objectives ο΅ Process Parameters/Variables: ο΅ Cutting Speed (π) ο΅ Feed (π) ο΅ Objectives: Minimize Cutting Forces ο΅ πΉπ₯ ο΅ πΉπ¦ ο΅ πΉπ§ Axial Forceπ π Thrust Forceπ π Cutting Forceπ π Tool Feed β πππππππ§π πΉπ₯ = 0.38607 β 0.01498 Γ π β 0.13220 Γ π + 0.0012 Γ π Γ π + 0.0003 Γ π2 + 0.019583 Γ π2 πππππππ§π πΉπ¦ = 0.25371 β 0.00375 Γ π β 0.12116 Γ π + 0.00063 Γ π Γ π + 0.000093 Γ π2 + 0.021 Γ π2 πππππππ§π πΉπ§ = 0.64841 + 0.078424 Γ π + 0.59493 Γ π β 0.0017 Γ π Γ π β 0.0009 Γ π2 β 0.0204 Γ π2 40 Anand and Patra (2018)
- 41. Solutions to πΉπ₯, πΉπ¦ & πΉπ§ Machining Process Objective Function Variations of CI Roulette Wheel Follow Best Follow Better Alienation Micro Drilling πΉπ₯ Mean 0.0569 0.0569 0.0569 0.0569 S.D. 0.0000 0.0000 0.0000 0.0000 Best 0.0569 0.0569 0.0569 0.0569 Run Time 0.73 0.94 0.78 0.79 πΉπ¦ Mean 0.0704 0.0704 0.0704 0.0704 S.D. 0.0000 0.0000 0.0000 0.0000 Best 0.0704 0.0704 0.0704 0.0704 Run Time 0.98 0.63 0.96 0.83 πΉπ§ Mean 2.2057 2.2057 2.2057 2.2057 S.D. 0.0000 0.0000 0.0000 0.0000 Best 2.2057 2.2057 2.2057 2.2057 Run Time 0.81 0.76 0.64 0.82 Obj Fun Experim ental Roulette Wheel Follow Best Follow Better Alienat ion πΉπ₯ 0.0950 0.0569 0.0569 0.0569 0.0569 πΉπ¦ 0.1080 0.0704 0.0704 0.0704 0.0704 πΉπ§ 0.1080 2.2057 2.2057 2.2057 2.2057 41
- 42. Micro-Turning 42
- 43. Micro-Turning: Optimization of Micro-turning Process ο΅ Cutting speed (ππ) ο΅ Feed (π) ο΅ Depth of cut (π ) ο΅ Objectives ο΅ Flank Wear (ππ) ο΅ Surface Roughness (π π) Tool feed π Cutting Speed ππ Flank wear ππ Depth of cut π πππππππ§π ππ = 0.004 Γ ππ 0.495 Γ π0.545 Γ π0.763 πππππππ§π π π = 0.048 Γ ππ β0.062 Γ π0.445 Γ π0.516 25 β€ ππ β€ 37 5 β€ π β€ 15 30 β€ π β€ 70 Durairaj and Gowri (2013) 43
- 44. Optimization of Micro-turning Process Process Cutter Dim Objective Function Algorithms Applied GA SA PSO Variations of CI Multi-CIroulette wheel fbest fbetter alienatio n Micro Turning 0.2 mm Nose radius π π Mean 0.45 0.45 0.46 0.46 0.45 0.45 0.46 0.46 S.D. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Best 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 Run Time 1.64 2.80 1.21 0.04 0.03 0.04 0.10 0.39 ππ Mean 0.63 0.64 0.63 0.63 0.63 0.63 0.63 0.63 S.D. 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Best 0.63 0.64 0.63 0.63 0.63 0.63 0.63 0.63 Run Time 1.42 2.78 0.89 0.06 0.04 0.04 0.10 0.38 44
- 45. For all the MATLAB Codes 45 www.sites.google.com/site/oatresearch/ or Google: OAT Research Lab
- 46. Thank you Anand J Kulkarni PhD, MASc, BEng, DME Associate Professor Symbiosis Center for Research and Innovation Symbiosis International (Deemed University) Pune 412 115, MH, India Email: anand.kulkarni@sitpune.edu.in kulk0003@ntu.edu.sg; anandmasc@gmail.com URL: sites.google.com/site/oatresearch/anand-jayant-kulkarni Ph: 91 20 6193 6790 ResearcherID: www.researcherid.com/rid/O-3585-2016 ORCID ID: orcid.org/0000-0001-6242-9492 Google Scholar: scholar.google.ca/citations?user=IAvtDokAAAAJ&hl=en 46